Practical curve fitting (original) (raw)

International Workshop on Modeling the Ocean , ECSB 1202 , PROGRAM

2010

s (Outstanding Young Scientist Award Presentations)................... p. 5-7 Abstracts (Regular Presentations, alphabetically)......................................... p. 8-19s (Regular Presentations, alphabetically)......................................... p. 8-19 List of Participants......................................................................................... p. 20-22 Maps.............................................................................................................. p. 23-24 Support and Help Provided by: Center for Coastal Physical Oceanography (CCPO) (http://www.ccpo.odu.edu/) Department of Ocean, Earth and Atmospheric Sciences (OEAS) (http://sci.odu.edu/oceanography/) Virginia Modeling, Analysis and Simulation Center (VMASC) (http://www.vmasc.odu.edu/) Virginia Applied Technology and Professional Development Center (VATPDC) (http://www.vatpdc.com/pdc/) Old Dominion University (http://www.odu.edu/) IWMO page: http://www.ccpo.odu.edu/\~tezer/IWMO\_2010/

Trigonometric Curve Fitting and an Application

In this study, information was given about sinusoidal functions, that is to say, trigonometric functions. A trigonometric curve fitting was performed on the basis of a data set so that error sum of squares could be minimum. The ratio of air relative humidity (%) was investigated during the time period between 02.

Application of Thin-Plate Splines in Two Dimensions to Oceanographic Tracer Data

Journal of Atmospheric and Oceanic Technology, 2011

This study explores the utility of the thin-plate spline (TPS) as a mapping procedure for oceanographic sections of bottle data in comparison with objective mapping (OM), sometimes referred to as objective interpolation. Standard OM techniques in oceanography require a priori assumptions about the structure of the errors associated with mapping when interpolating irregularly spaced data. Alternatively, the TPS can be used to approximate mapping errors by fitting a nonparametric model using multiple covariates with a less rigid, physically consistent, spatial correlation structure. The case is made that these errors reflect the sparsity of the data coverage and quantify mapping error better than the estimates using OM. It is demonstrated that the maps from the TPS recreate the essential large-scale features of chlorofluorocarbon- or freon-11 (CFC-11) concentrations and inferred “ages,” but smooth over smaller-scale features, such as eddies. The TPS can outperform OM when either the d...